
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


#TAKES A LONG FUCKING TIME
firedf = pd.read_csv("Fire_Department_Calls_for_Service_20240414.csv")

C:\Users\ALW\AppData\Local\Temp\ipykernel_18024\1740023720.py:2: DtypeWarning: Columns (19,20,25) have mixed types. Specify dtype option on import or set low_memory=False.
  firedf = pd.read_csv("Fire_Department_Calls_for_Service_20240414.csv")


wanted_call_types = ["Alarms", "Electrical Hazard", "Explosion", "Lightning Strike (Investigation)", "Marine Fire", "Outside Fire", 
                     "Smoke Investigation (Outside)", "Structure Fire", "Train / Rail Fire", "Vehicle Fire"]

firedf = firedf[firedf["Call Type"].isin(wanted_call_types)]
firedf['Call Date'] = pd.to_datetime(firedf['Call Date'])
firedf.index = firedf["Call Date"]
firedf = firedf.sort_index()
firedf = firedf.loc["2019-01-01":"2023-12-31"]
firedf = firedf[firedf["Received DtTm"].notnull()]
firedf = firedf[firedf["On Scene DtTm"].notnull()]
firedf = firedf[firedf["Neighborhooods - Analysis Boundaries"].notnull()]
firedf = firedf[~firedf["Neighborhooods - Analysis Boundaries"].isin(["Treasure Island"])]
firedf["Neighborhooods - Analysis Boundaries"] = firedf["Neighborhooods - Analysis Boundaries"].replace({"Hayes Valley": "Western Addition",
                                                        "Lone Mountain/USF": "Inner Richmond",
                                                        "McLaren Park": "Visitacion Valley","Japantown": "Western Addition",
                                                        "Lincoln Park": "Seacliff","Oceanview/Merced/Ingleside": "Ocean View",
                                                        "Financial District/South Beach": "Financial District","Portola": "Excelsior",
                                                        "Tenderloin": "Downtown/Civic Center","Mission Bay": "South of Market",
                                                        "Bayview Hunters Point": "Bayview"})
firedf = firedf[firedf["Received DtTm"] != firedf["On Scene DtTm"]]
received_series = pd.to_datetime(firedf["Received DtTm"])
on_scene_series = pd.to_datetime(firedf["On Scene DtTm"])
response_times = (on_scene_series-received_series).astype('timedelta64[ns]')
firedf["Response times"] = response_times
firedf = firedf[response_times.dt.total_seconds() > 0]
firedf.index = pd.to_datetime(firedf["Received DtTm"])
firedf = firedf.sort_index()
firedf


def convert_point_to_tuple(point_entry):
    return eval(",".join(point_entry.split("POINT ")[1].split(" ")))
rentdf = pd.read_csv("Rent_Ordinance_Housing_Inventory_20240505.csv")
rentdf = rentdf[rentdf["submission_year"]==2023]
rentdf = rentdf[rentdf["occupancy_type"]=="Occupied by non-owner"]
rentdf = rentdf[rentdf["analysis_neighborhood"].notnull()]
rentdf["analysis_neighborhood"] = rentdf["analysis_neighborhood"].replace({"Hayes Valley": "Western Addition",
                                                        "Lone Mountain/USF": "Inner Richmond",
                                                        "McLaren Park": "Visitacion Valley","Japantown": "Western Addition",
                                                        "Lincoln Park": "Seacliff","Oceanview/Merced/Ingleside": "Ocean View",
                                                        "Financial District/South Beach": "Financial District","Portola": "Excelsior",
                                                        "Tenderloin": "Downtown/Civic Center","Mission Bay": "South of Market",
                                                        "Bayview Hunters Point": "Bayview"})
rentdf = rentdf[rentdf["point"].notnull()]
rentdf = rentdf[rentdf["monthly_rent"]!="$0 (no rent paid by the occupant)"]
rentdf = rentdf[rentdf["square_footage"]!="Unknown"]
rentdf["point"] = rentdf["point"].map(convert_point_to_tuple)
def get_mean_rent(elem):
    numbers = elem.split("-$")
    if len(numbers)==1:
        numbers[0]=int(numbers[0].replace("$","").replace("+",""))
        return int(numbers[0])
    elif len(numbers)==2:
        numbers[0]=int(numbers[0].replace("$","").replace("+",""))
        numbers[1]=int(numbers[1].replace("$","").replace("+",""))
        return sum(numbers)/2
    else:
        raise Exception(f"wtf? {elem} {numbers}")
    
rentdf["true_monthly_mean"] = rentdf["monthly_rent"].map(get_mean_rent)
def get_mean_footage(elem):
    numbers=elem.split(" Sq.Ft")[0].split("-")
    if len(numbers)==1:
        numbers[0] = numbers[0].split("+")[0]
        return int(numbers[0])
    elif len(numbers)==2:
        numbers[0]=int(numbers[0])
        numbers[1]=int(numbers[1])
        return sum(numbers)/2

rentdf["true_square_footage"] = rentdf["square_footage"].map(get_mean_footage)
rentdf


districts = ["Golden Gate Park","Sunset/Parkside","Seacliff","Presidio","Outer Richmond","Inner Sunset","Haight Ashbury","Presidio Heights",
             "Inner Richmond","North Beach","Russian Hill","Nob Hill","Chinatown","Downtown/Civic Center","Financial District","Marina",
             "Western Addition","Pacific Heights","South of Market","Mission","Bayview","Potrero Hill","Bernal Heights","Excelsior",
             "Visitacion Valley","Ocean View","Lakeshore","Glen Park","Twin Peaks","Castro/Upper Market","Noe Valley","Outer Mission",
             "West of Twin Peaks"] 
districtdict = {}
for district in districts:

    districtdf = firedf[firedf["Neighborhooods - Analysis Boundaries"]==district]
    rentdistrictdf = rentdf[rentdf["analysis_neighborhood"]==district]
    d_r_t = districtdf["Response times"]
    meanrent = np.mean(rentdistrictdf["true_monthly_mean"])
    meanfootage = np.mean(rentdistrictdf["true_square_footage"])
    mean_per_mean = meanrent/meanfootage if district!= "Presidio" else None
    districtdict.update({district: [np.mean(d_r_t.dt.total_seconds()/60),min(d_r_t.dt.total_seconds()/60),max(d_r_t.dt.total_seconds()/60),mean_per_mean]}) 

district_df = pd.DataFrame.from_dict(districtdict,orient="index")
district_df = district_df.reset_index()
district_df = district_df.rename(columns={"index": "NEIGHBORHO", 0: "Mean response time (min)", 1: "Fastest response time", 2:"Slowest response time",
                                          3:"Mean rent ($/ft^2)"})
district_df


tempdf = district_df.sort_values(by=["Mean response time (min)"])
tempdf["Speed rank"]=list(range(0,len(district_df)))
district_df = tempdf.sort_index()

tempdf = district_df.sort_values(by=["Mean rent ($/ft^2)"])
tempdf["Rent rank"]=list(range(len(district_df)-1,-1,-1))
district_df = tempdf.sort_index()
district_df


def convert_point_to_tuple(point_entry):
    return eval(",".join(point_entry.split("POINT ")[1].split(" ")))
rentdf = pd.read_csv("Rent_Ordinance_Housing_Inventory_20240505.csv")
rentdf = rentdf[rentdf["submission_year"]==2023]
rentdf = rentdf[rentdf["occupancy_type"]=="Occupied by non-owner"]
rentdf = rentdf[rentdf["analysis_neighborhood"].notnull()]
rentdf["analysis_neighborhood"] = rentdf["analysis_neighborhood"].replace({"Hayes Valley": "Western Addition",
                                                        "Lone Mountain/USF": "Inner Richmond",
                                                        "McLaren Park": "Visitacion Valley","Japantown": "Western Addition",
                                                        "Lincoln Park": "Seacliff","Oceanview/Merced/Ingleside": "Ocean View",
                                                        "Financial District/South Beach": "Financial District","Portola": "Excelsior",
                                                        "Tenderloin": "Downtown/Civic Center","Mission Bay": "South of Market",
                                                        "Bayview Hunters Point": "Bayview"})
rentdf = rentdf[rentdf["point"].notnull()]
rentdf = rentdf[rentdf["monthly_rent"]!="$0 (no rent paid by the occupant)"]
rentdf = rentdf[rentdf["square_footage"]!="Unknown"]
rentdf["point"] = rentdf["point"].map(convert_point_to_tuple)
def get_mean_rent(elem):
    numbers = elem.split("-$")
    if len(numbers)==1:
        numbers[0]=int(numbers[0].replace("$","").replace("+",""))
        return int(numbers[0])
    elif len(numbers)==2:
        numbers[0]=int(numbers[0].replace("$","").replace("+",""))
        numbers[1]=int(numbers[1].replace("$","").replace("+",""))
        return sum(numbers)/2
    else:
        raise Exception(f"wtf? {elem} {numbers}")
    
rentdf["true_monthly_mean"] = rentdf["monthly_rent"].map(get_mean_rent)
def get_mean_footage(elem):
    numbers=elem.split(" Sq.Ft")[0].split("-")
    if len(numbers)==1:
        numbers[0] = numbers[0].split("+")[0]
        return int(numbers[0])
    elif len(numbers)==2:
        numbers[0]=int(numbers[0])
        numbers[1]=int(numbers[1])
        return sum(numbers)/2

rentdf["true_square_footage"] = rentdf["square_footage"].map(get_mean_footage)
rentdf


months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
monthsnums = list(range(0, 12, 1))

num_rows = len(wanted_call_types) // 2 + len(wanted_call_types) % 2
num_cols = 2
fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, 30))
plt.subplots_adjust(left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2, hspace=0.5)  # 调整布局参数

for i, calltype in enumerate(wanted_call_types):
    calltypedf = firedf[firedf["Call Type"] == calltype]
    monthdict = {}
    for j, month in enumerate(months):
        monthdict.update({month: len(calltypedf[calltypedf.index.month == j + 1])})
        
    plot_df = pd.DataFrame([monthdict])
    ax = axes[i // num_cols, i % num_cols]
    plot_df.iloc[0].plot.bar(ax=ax)
    ax.set_ylabel("Number of incidents")
    ax.set_xlabel("Month")
    ax.set_xticks(monthsnums)
    ax.set_xticklabels(months)
    ax.set_title(f"Number of Calls of Type: '{calltype}'")

plt.show()


hournums = list(range(0, 24, 1))
num_rows = len(wanted_call_types) // 2 + len(wanted_call_types) % 2
num_cols = 2
fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 30))
plt.subplots_adjust(left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2, hspace=0.5)

for i, calltype in enumerate(wanted_call_types):
    calltypedf = firedf[firedf["Call Type"] == calltype]
    hourdict = {}
    for hour in hournums:
        hourdict.update({hour: len(calltypedf[calltypedf.index.hour==hour])})
        
    plot_df = pd.DataFrame([hourdict])
    ax = axes[i // num_cols, i % num_cols]
    plot_df.iloc[0].plot.bar(ax=ax)
    ax.set_ylabel("Number of incidents")
    ax.set_xlabel("Hour of day")
    ax.set_title(f"Number of Calls of Type: '{calltype}'")

plt.show()


yearnums = list(range(2019, 2023+1, 1))
num_rows = 1
num_cols = 1

datadict = {year: [0 for _ in wanted_call_types] for year in yearnums}

for year in yearnums:
    for i, calltype in enumerate(wanted_call_types):
        calltypedf = firedf[firedf["Call Type"] == calltype]
        datadict[year][i] = len(calltypedf[calltypedf.index.year==year])


plot_df = pd.DataFrame.from_dict(datadict,orient='index',columns=wanted_call_types)
    
# ax = axes
# plot_df.iloc[0].plot.bar(ax=ax,stacked=True)
# ax.set_ylabel("Number of incidents")
# ax.set_xlabel("Year")
# ax.set_title(f"Number of Calls")

# plt.show()
ax = plot_df.plot.bar(stacked=True,colormap="tab20b")
ax.set_ylabel("Number of incidents")
ax.set_xlabel("Year")
ax.set_title(f"Numbers of Fire Incidents from 2019 to 2023")
leg = plt.legend( loc = 'upper right')

plt.draw() # Draw the figure so you can find the positon of the legend. 

# Get the bounding box of the original legend
bb = leg.get_bbox_to_anchor().transformed(ax.transAxes.inverted())

# Change to location of the legend. 
xOffset = 0.6
bb.x0 += xOffset
bb.x1 += xOffset
leg.set_bbox_to_anchor(bb, transform = ax.transAxes)

plt.show()


import plotly.express as px
import plotly.io as pio

import geopandas as gpd 
import shapely
gdf = gpd.read_file("ark28722-s75c8t-geojson.json")
gdf.to_crs(epsg=4326, inplace=True)
merge_gdf1 = gdf[gdf["NEIGHBORHO"].isin(["Outer Sunset", "Parkside"])]
merge_gdf2 = gdf[gdf["NEIGHBORHO"].isin(["Glen Park", "Diamond Heights"])]
merge_gdf3 = gdf[gdf["NEIGHBORHO"].isin(["Visitacion Valley", "Crocker Amazon"])]
gdf = gdf[~gdf["NEIGHBORHO"].isin(["Crocker Amazon","Parkside","Diamond Heights", "Parkside"])]
gdf[gdf["NEIGHBORHO"]=="Outer Sunset"]=["s75c8t.2",2,"Sunset/Parkside",shapely.unary_union(merge_gdf1["geometry"])]
gdf[gdf["NEIGHBORHO"]=="Glen Park"]=["s75c8t.30",30,"Glen Park",shapely.unary_union(merge_gdf2["geometry"])]
gdf[gdf["NEIGHBORHO"]=="Visitacion Valley"]=["s75c8t.25",25,"Visitacion Valley",shapely.unary_union(merge_gdf3["geometry"])]
gdf.set_index('NEIGHBORHO', inplace=True)

fig = px.choropleth_mapbox(district_df, geojson=gdf["geometry"], locations=gdf.index, color='Mean response time (min)',
                           color_continuous_scale="temps",hover_data = ["Speed rank"],
                           #range_color=(0, 12),
                           mapbox_style="carto-positron",
                           zoom=11, center = {"lat": 37.773972, "lon": -122.431297},
                           opacity=0.5,
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
pio.write_html(fig, file='map_t.html', auto_open=True)


fig = px.choropleth_mapbox(district_df, geojson=gdf["geometry"], locations=gdf.index, color='Mean rent ($/ft^2)',
                           color_continuous_scale="temps",hover_data = ["Rent rank"],
                           #range_color=(0, 12),
                           mapbox_style="carto-positron",
                           zoom=11, center = {"lat": 37.773972, "lon": -122.431297},
                           opacity=0.5,
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
pio.write_html(fig, file='map_r.html', auto_open=True)

	Call Number	Unit ID	Incident Number	Call Type	Call Date	Watch Date	Received DtTm	Entry DtTm	Dispatch DtTm	Response DtTm	...	Unit sequence in call dispatch	Fire Prevention District	Supervisor District	Neighborhooods - Analysis Boundaries	RowID	case_location	data_as_of	data_loaded_at	Analysis Neighborhoods	Response times
Received DtTm
2019-01-01 00:07:30	190010050	B08	19000005	Outside Fire	2019-01-01	12/31/2018	01/01/2019 12:07:30 AM	01/01/2019 12:08:16 AM	01/01/2019 12:08:34 AM	01/01/2019 12:10:44 AM	...	2.0	9.0	7.0	West of Twin Peaks	190010050-B08	POINT (-122.45801 37.736103)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	41.0	0 days 00:06:56
2019-01-01 00:07:30	190010050	E40	19000005	Outside Fire	2019-01-01	12/31/2018	01/01/2019 12:07:30 AM	01/01/2019 12:08:16 AM	01/01/2019 12:08:34 AM	01/01/2019 12:12:11 AM	...	6.0	9.0	7.0	West of Twin Peaks	190010050-E40	POINT (-122.45801 37.736103)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	41.0	0 days 00:11:29
2019-01-01 00:07:30	190010050	T19	19000005	Outside Fire	2019-01-01	12/31/2018	01/01/2019 12:07:30 AM	01/01/2019 12:08:16 AM	01/01/2019 12:08:34 AM	01/01/2019 12:09:50 AM	...	5.0	9.0	7.0	West of Twin Peaks	190010050-T19	POINT (-122.45801 37.736103)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	41.0	0 days 00:09:44
2019-01-01 00:07:30	190010050	E15	19000005	Outside Fire	2019-01-01	12/31/2018	01/01/2019 12:07:30 AM	01/01/2019 12:08:16 AM	01/01/2019 12:08:34 AM	01/01/2019 12:10:15 AM	...	7.0	9.0	7.0	West of Twin Peaks	190010050-E15	POINT (-122.45801 37.736103)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	41.0	0 days 00:27:03
2019-01-01 00:07:30	190010050	E39	19000005	Outside Fire	2019-01-01	12/31/2018	01/01/2019 12:07:30 AM	01/01/2019 12:08:16 AM	01/01/2019 12:08:34 AM	01/01/2019 12:10:11 AM	...	1.0	9.0	7.0	West of Twin Peaks	190010050-E39	POINT (-122.45801 37.736103)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	41.0	0 days 00:06:39
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-12-31 23:08:10	233653065	E03	23177194	Alarms	2023-12-31	12/31/2023	12/31/2023 11:08:10 PM	12/31/2023 11:09:21 PM	12/31/2023 11:09:28 PM	12/31/2023 11:10:16 PM	...	1.0	2.0	6.0	Downtown/Civic Center	233653065-E03	POINT (-122.41424 37.783722)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	36.0	0 days 00:04:46
2023-12-31 23:30:19	233653128	B01	23177205	Alarms	2023-12-31	12/31/2023	12/31/2023 11:30:19 PM	12/31/2023 11:32:04 PM	12/31/2023 11:33:59 PM	12/31/2023 11:36:07 PM	...	3.0	1.0	3.0	North Beach	233653128-B01	POINT (-122.40815 37.803432)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	23.0	0 days 00:09:13
2023-12-31 23:30:19	233653128	E28	23177205	Alarms	2023-12-31	12/31/2023	12/31/2023 11:30:19 PM	12/31/2023 11:32:04 PM	12/31/2023 11:33:59 PM	12/31/2023 11:35:24 PM	...	1.0	1.0	3.0	North Beach	233653128-E28	POINT (-122.40815 37.803432)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	23.0	0 days 00:06:03
2023-12-31 23:30:19	233653128	T02	23177205	Alarms	2023-12-31	12/31/2023	12/31/2023 11:30:19 PM	12/31/2023 11:32:04 PM	12/31/2023 11:33:59 PM	12/31/2023 11:36:17 PM	...	2.0	1.0	3.0	North Beach	233653128-T02	POINT (-122.40815 37.803432)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	23.0	0 days 00:08:56
2023-12-31 23:32:43	233653133	E21	23177207	Outside Fire	2023-12-31	12/31/2023	12/31/2023 11:32:43 PM	12/31/2023 11:33:21 PM	12/31/2023 11:34:33 PM	12/31/2023 11:35:17 PM	...	1.0	5.0	1.0	Inner Richmond	233653133-E21	POINT (-122.453 37.77497)	02/05/2024 03:27:52 AM	02/05/2024 10:56:25 AM	18.0	0 days 00:05:48

	unique_id	block_num	unit_count	case_type_name	submission_year	block_address	occupancy_type	occupancy_or_vacancy_date	occupancy_or_vacancy_date_year	bedroom_count	...	signature_date	occupancy_or_vacancy_date_history	year_property_built	point	analysis_neighborhood	supervisor_district	data_as_of	data_loaded_at	true_monthly_mean	true_square_footage
0	5784430458147580041	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2021/03/17	2021	Two-Bedroom	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	3125.5	875.5
1	4954451792168111171	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2016/03/12	2016	Studio	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1375.5	375.5
2	-5063247231305562579	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2021/12/15	2021	Studio	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	2375.5	625.5
3	-9079354581068241121	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2010/02/01	2010	One-Bedroom	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1875.5	875.5
4	756572995386429446	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2004/06/26	2004	One-Bedroom	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	2875.5	625.5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
152093	4047857095214640194	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2022/10/27	2022	Studio	...	2023/02/17	[\n {\n "date_range_type": "Occupied",\n ...	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1375.5	125.0
152094	8790490401777245212	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2017/12/14	2017	Studio	...	2023/02/17	NaN	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1125.5	125.0
152095	4275828707947196532	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2015/04/23	2015	Studio	...	2023/02/17	NaN	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1125.5	125.0
152096	1350372243345719808	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2020/08/07	2020	Studio	...	2023/02/17	NaN	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1125.5	125.0
152097	6894355032800143611	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2022/11/15	2022	Studio	...	2023/02/17	[\n {\n "date_range_type": "Occupied",\n ...	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1375.5	125.0

	NEIGHBORHO	Mean response time (min)	Fastest response time	Slowest response time	Mean rent ($/ft^2)
0	Golden Gate Park	8.285412	1.250000	281.716667	4.396416
1	Sunset/Parkside	7.792845	0.350000	263.350000	2.659374
2	Seacliff	8.268844	0.800000	82.200000	2.669059
3	Presidio	8.767269	1.966667	135.550000	NaN
4	Outer Richmond	7.193393	0.100000	107.650000	2.745444
5	Inner Sunset	7.218015	1.533333	90.300000	3.124437
6	Haight Ashbury	7.040618	0.100000	95.016667	3.419354
7	Presidio Heights	6.909119	1.116667	240.266667	3.208674
8	Inner Richmond	6.985809	1.633333	152.250000	3.050679
9	North Beach	6.732717	1.550000	55.416667	3.793911
10	Russian Hill	7.052900	0.950000	205.633333	3.951208
11	Nob Hill	6.352649	0.900000	138.466667	3.588992
12	Chinatown	6.167121	0.583333	72.483333	3.294388
13	Downtown/Civic Center	6.490172	0.516667	113.683333	3.687266
14	Financial District	7.342674	0.200000	191.316667	4.235989
15	Marina	7.030896	0.783333	52.766667	3.773530
16	Western Addition	6.349658	0.066667	222.666667	3.583172
17	Pacific Heights	6.276151	0.516667	73.066667	3.510876
18	South of Market	6.771137	0.333333	107.500000	3.879805
19	Mission	6.761253	0.233333	224.433333	3.116139
20	Bayview	8.551874	0.083333	199.183333	2.458615
21	Potrero Hill	7.868106	1.450000	256.983333	3.640064
22	Bernal Heights	7.967816	0.900000	215.566667	3.249949
23	Excelsior	8.031674	1.050000	103.316667	2.439387
24	Visitacion Valley	10.061952	1.383333	68.666667	2.369647
25	Ocean View	7.682359	0.283333	249.150000	2.523348
26	Lakeshore	7.815318	1.800000	112.966667	2.577835
27	Glen Park	8.137615	1.200000	103.716667	3.069781
28	Twin Peaks	8.480395	1.366667	61.500000	3.125427
29	Castro/Upper Market	6.741658	0.766667	207.516667	3.412938
30	Noe Valley	6.838826	0.683333	246.000000	3.442789
31	Outer Mission	7.867571	0.900000	180.500000	2.986147
32	West of Twin Peaks	7.633971	0.066667	111.933333	2.808919

	NEIGHBORHO	Mean response time (min)	Fastest response time	Slowest response time	Mean rent ($/ft^2)	Speed rank	Rent rank
0	Golden Gate Park	8.285412	1.250000	281.716667	4.396416	28	1
1	Sunset/Parkside	7.792845	0.350000	263.350000	2.659374	20	27
2	Seacliff	8.268844	0.800000	82.200000	2.669059	27	26
3	Presidio	8.767269	1.966667	135.550000	NaN	31	0
4	Outer Richmond	7.193393	0.100000	107.650000	2.745444	15	25
5	Inner Sunset	7.218015	1.533333	90.300000	3.124437	16	19
6	Haight Ashbury	7.040618	0.100000	95.016667	3.419354	13	13
7	Presidio Heights	6.909119	1.116667	240.266667	3.208674	10	17
8	Inner Richmond	6.985809	1.633333	152.250000	3.050679	11	22
9	North Beach	6.732717	1.550000	55.416667	3.793911	5	5
10	Russian Hill	7.052900	0.950000	205.633333	3.951208	14	3
11	Nob Hill	6.352649	0.900000	138.466667	3.588992	3	9
12	Chinatown	6.167121	0.583333	72.483333	3.294388	0	15
13	Downtown/Civic Center	6.490172	0.516667	113.683333	3.687266	4	7
14	Financial District	7.342674	0.200000	191.316667	4.235989	17	2
15	Marina	7.030896	0.783333	52.766667	3.773530	12	6
16	Western Addition	6.349658	0.066667	222.666667	3.583172	2	10
17	Pacific Heights	6.276151	0.516667	73.066667	3.510876	1	11
18	South of Market	6.771137	0.333333	107.500000	3.879805	8	4
19	Mission	6.761253	0.233333	224.433333	3.116139	7	20
20	Bayview	8.551874	0.083333	199.183333	2.458615	30	30
21	Potrero Hill	7.868106	1.450000	256.983333	3.640064	23	8
22	Bernal Heights	7.967816	0.900000	215.566667	3.249949	24	16
23	Excelsior	8.031674	1.050000	103.316667	2.439387	25	31
24	Visitacion Valley	10.061952	1.383333	68.666667	2.369647	32	32
25	Ocean View	7.682359	0.283333	249.150000	2.523348	19	29
26	Lakeshore	7.815318	1.800000	112.966667	2.577835	21	28
27	Glen Park	8.137615	1.200000	103.716667	3.069781	26	21
28	Twin Peaks	8.480395	1.366667	61.500000	3.125427	29	18
29	Castro/Upper Market	6.741658	0.766667	207.516667	3.412938	6	14
30	Noe Valley	6.838826	0.683333	246.000000	3.442789	9	12
31	Outer Mission	7.867571	0.900000	180.500000	2.986147	22	23
32	West of Twin Peaks	7.633971	0.066667	111.933333	2.808919	18	24

	unique_id	block_num	unit_count	case_type_name	submission_year	block_address	occupancy_type	occupancy_or_vacancy_date	occupancy_or_vacancy_date_year	bedroom_count	...	signature_date	occupancy_or_vacancy_date_history	year_property_built	point	analysis_neighborhood	supervisor_district	data_as_of	data_loaded_at	true_monthly_mean	true_square_footage
0	5784430458147580041	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2021/03/17	2021	Two-Bedroom	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	3125.5	875.5
1	4954451792168111171	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2016/03/12	2016	Studio	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1375.5	375.5
2	-5063247231305562579	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2021/12/15	2021	Studio	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	2375.5	625.5
3	-9079354581068241121	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2010/02/01	2010	One-Bedroom	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1875.5	875.5
4	756572995386429446	0750	142	Housing Inventory - Unit information (2023)	2023	1400 Block of TURK ST	Occupied by non-owner	2004/06/26	2004	One-Bedroom	...	2023/02/28	NaN	1993.0	(-122.432973442, 37.780445712)	Western Addition	5.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	2875.5	625.5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
152093	4047857095214640194	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2022/10/27	2022	Studio	...	2023/02/17	[\n {\n "date_range_type": "Occupied",\n ...	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1375.5	125.0
152094	8790490401777245212	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2017/12/14	2017	Studio	...	2023/02/17	NaN	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1125.5	125.0
152095	4275828707947196532	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2015/04/23	2015	Studio	...	2023/02/17	NaN	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1125.5	125.0
152096	1350372243345719808	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2020/08/07	2020	Studio	...	2023/02/17	NaN	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1125.5	125.0
152097	6894355032800143611	0065	74	Housing Inventory - Unit information (2023)	2023	900 Block of COLUMBUS AVE	Occupied by non-owner	2022/11/15	2022	Studio	...	2023/02/17	[\n {\n "date_range_type": "Occupied",\n ...	1916.0	(-122.414343193, 37.80310132)	Russian Hill	3.0	2024/05/04 12:00:00 AM	2024/05/05 06:07:07 AM	1375.5	125.0

The Explainer Notebook - Group 38¶

Ying Lu (s232177) and Susanna Porcelli (s232698)¶

Motivation¶

What is your dataset?¶

What was your goal for the end user's experience?¶

Basic stats¶

Write about your choices in data cleaning and preprocessing¶

Write a short section that discusses the dataset stats, containing key points/plots from your exploratory data analysis.¶

Data Analysis¶

Describe your data analysis and explain what you've learned about the dataset.¶

If relevant, talk about your machine-learning.¶

Genre¶

Which genre of data story did you use?¶

Which tools did you use from each of the 3 categories of Visual Narrative (Figure 7 in Segal and Heer). Why?¶

Which tools did you use from each of the 3 categories of Narrative Structure (Figure 7 in Segal and Heer). Why?¶

Visualizations¶

Discussion¶

Contributions¶